What To Do (And Not to Do) with Modeling Proportions/Fractional Outcomes {https://t.co/d8rTDTIAkA} #rstats #DataScience
— R-bloggers (@Rbloggers) June 27, 2022
Shiny and Arrow {https://t.co/9jVwlA2mgC} #rstats #DataScience
— R-bloggers (@Rbloggers) June 27, 2022
How to Join Multiple Data Frames in R {https://t.co/uz3Nxd2P3k} #rstats #DataScience
— R-bloggers (@Rbloggers) June 30, 2022
Replace NA with Zero in R {https://t.co/6PtKxNd8uB} #rstats #DataScience
— R-bloggers (@Rbloggers) June 29, 2022
A Major Contribution to Learning R {https://t.co/qGvMdDUbg6} #rstats #DataScience
— R-bloggers (@Rbloggers) July 2, 2022
10 ways you can provide value with RStudio Connect {https://t.co/29kqdDbyuF} #rstats #DataScience
— R-bloggers (@Rbloggers) June 28, 2022
Model-Based Causal Forests for Heterogeneous Treatment Effects {https://t.co/UuPf5ydmyU} #rstats #DataScience
— R-bloggers (@Rbloggers) July 2, 2022
Food Crisis Analysis and, Forecasting with Neural Network Autoregression {https://t.co/pTM8vj913j} #rstats #DataScience
— R-bloggers (@Rbloggers) June 30, 2022
UseR!2022: Best Practices
— R-bloggers (@Rbloggers) June 29, 2022
for Shiny Apps with Docker and More {https://t.co/fnsQu3yKbv} #rstats #DataScience
An introductory course in Shiny – July sessions {https://t.co/M88nQw71gt} #rstats #DataScience
— R-bloggers (@Rbloggers) June 29, 2022
An introductory course in Shiny – July sessions {https://t.co/sRsgLATVuP} #rstats #DataScience
— R-bloggers (@Rbloggers) July 1, 2022
RObservations #34: Using NLP with keras to understand market sentiment with LSTM networks {https://t.co/2zwhicMr5U} #rstats #DataScience
— R-bloggers (@Rbloggers) June 30, 2022
Survival Analysis in R (in under 10-minutes) {https://t.co/cNdGBIoDj8} #rstats #DataScience
— R-bloggers (@Rbloggers) June 9, 2022
An introductory course in Shiny {https://t.co/MWxFJMO4uY} #rstats #DataScience
— R-bloggers (@Rbloggers) June 26, 2022
Creating flowcharts with {ggplot2} {https://t.co/2TGoUehuJE} #rstats #DataScience
— R-bloggers (@Rbloggers) June 7, 2022
Automated Survey Reporting With googlesheets4, pins, and R Markdown {https://t.co/O9WdYNA6eO} #rstats #DataScience
— R-bloggers (@Rbloggers) June 15, 2022
Webscraping in R with Rvest {https://t.co/VuzCHurZQ7} #rstats #DataScience
— R-bloggers (@Rbloggers) June 25, 2022
What To Do (And Not to Do) with Modeling Proportions/Fractional Outcomes {https://t.co/d8rTDTIAkA} #rstats #DataScience
— R-bloggers (@Rbloggers) June 27, 2022
The Poisson distribution: From basic probability theory to regression models {https://t.co/lxZvYTrLtB} #rstats #DataScience
— R-bloggers (@Rbloggers) June 23, 2022
How to Group and Summarize Data in R {https://t.co/6PPqmthbJL} #rstats #DataScience
— R-bloggers (@Rbloggers) June 26, 2022
Crosstab calculation in R {https://t.co/xXgclJ9gO1} #rstats #DataScience
— R-bloggers (@Rbloggers) June 11, 2022
R vs Python — Live Stream Analysis {https://t.co/NA0lcd6jtT} #rstats #DataScience
— R-bloggers (@Rbloggers) June 7, 2022
Filtering for Unique Values in R- Using the dplyr {https://t.co/US1O9YE7TB} #rstats #DataScience
— R-bloggers (@Rbloggers) June 12, 2022
Timing data.table Operations {https://t.co/rRCaMrJfQ8} #rstats #DataScience
— R-bloggers (@Rbloggers) June 11, 2022
---
title: "RBloggers Top Tweets"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(httr)
library(lubridate)
library(jsonlite)
library(purrr)
rbloggers <- fromJSON("data/rbloggers.json")
get_tweet_embed <- function(user, status_id) {
url <-
stringr::str_glue(
"https://publish.twitter.com/oembed?url=https://twitter.com/{user}/status/{status_id}&partner=&hide_thread=false"
)
response <- GET(url) %>%
content()
return(shiny::HTML(response$html))
}
```
Column {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Top Tweets - 7 days {.tweet-wall}
```{r}
rblog_7 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 7, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_7_html <-
map2_chr(rblog_7$screen_name, rblog_7$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_7_html}"))
```
### Top Tweets - 30 days {.tweet-wall}
```{r}
rblog_30 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 30, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_30_html <-
map2_chr(rblog_30$screen_name, rblog_30$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_30_html}"))
```